In many fields, (for example the fields of threat detection, object detection, and classification) there is a need for the extraction, fusion, analysis, and visualisation of large sets of data.
In many situations, such large sets of data may comprise data sets from multiple heterogeneous data sources, i.e. data sources that are independent and that produce dissimilar data sets. Heterogeneous data sources may, for example, use different terminology, units of measurement, domains, scopes, and provide different data types (e.g. binary, discrete, categorical, interval, probabilistic, and linguistic data types).
A Relevance Vector Machine (RVM) is a machine learning technique that can be used to process large data sets and provide inferences at relatively low computational cost.
However, with the conventional RVM, a number of basis functions needs to be provided a priori. Thus, conventional RVM techniques tend to have limited flexibility. Typically the RVM is trained using a set of training data comprising inputs and corresponding outputs of a system. However, the conventional RVM tends to fail to adequately model the uncertainty about an output corresponding to an input that is ‘far away’ from the inputs in the set of training data.